Nihal Fayez F. Areed

Reducing material toxicity remains a major challenge in the development of high-efficiency perovskite solar cells (PSCs). This study investigates several single and double-absorber PSC structures to identify an optimum structure balancing low toxicity and high-power conversion efficiency (PCE). A novel half-tandem dual-absorber PSC structure consisting of ITO/ SnO₂/ Cs₂TiBr₆/ Cs₂AgBiI₆/ P3HT/ NiO_x/ Au was proposed and analyzed using finite element method (FEM). Additionally, the influence of absorbers thickness, doping and defect densities was examined. Results demonstrate that introducing 400 nm layers of Cs₂AgBiI₆ and Cs₂TiBr₆ as main dual absorbers improves the PSC’s performance, increasing short circuit current density and PCE to 31.89 mA/cm², and 32.57 %, respectively due to 19.88 % and 22.48 % escalation in absorption and carrier generation rate compared to conventional MAPbI₃ PSC. Furthermore, optimizing the thickness and defect density of both Cs₂TiBr₆ upper absorber to 60 nm and 1×10¹² cm^-3, and Cs₂AgBiI₆ lower absorber to 1200 nm and 1×10¹⁴ cm^-3, respectively, yielded superior performance. The optimal PSC dual absorber structure provided in this study accomplishes short circuit current density of 34.14 mA/cm², open circuit voltage of 1.23 V, and fill factor of 87.82 % with a simulated PCE of 36.87 %, which is theoretically 14.62 % higher than the previously reported PCE value of conventional MAPbI₃ PSC. These findings confirm that the proposed Cs₂TiBr₆-Cs₂AgBiI₆ dual-absorber PSC structure offers a promising non-toxic alternative to the conventional toxic cells with strong potential for future photovoltaic applications.

The metal-insulator transition (MIT) of vanadium dioxide (VO₂) exhibits a great change in its complex refractive index. This is a key property for the design of ultra-compact photonic devices. In this paper, highly efficient TM pass polarizer and TE modulator are reported and numerically analyzed based on diffused waveguide with a double stack of VO₂/PMMA layers. The idea here depends on achieving significant different mode profiles for orthogonal polarizations thanks to the MIT nature of VO₂ that can be extensively used in polarization-selective mode shaping. The two-dimensional finite element method (2D-FEM) is utilized to carry out the analysis of the reported device while finite difference time domain method (FDTD) is employed for investigating the light propagation through the proposed structure. The numerical results for a given device length of 50 μm at the telecommunication wavelength of 1.55 μm offers polarization extinction ratio (PER) of 20.8 dB and an insertion loss (IL) of about 0.089 dB for the TM pass polarizer during insulating phase. Changing to metallic phase, an extinction ratio (ER) as high as 20.4 dB and IL of 0.5 dB are achieved for the TE modulator. The presented polarizer/modulator has the advantages of large PER/ER, low IL and fabrication feasibility. Thus, it may be a good candidate for integrated photonics applications.

This article presents a highly sensitive and thermally stable photonic crystal (PhC) biosensor designed for accurate cancer-cell detection. The proposed sensor features a square-lattice of silicon rods (radius 0.1 µm) with a photonic bandgap spanning 1.2–2.1 µm. It includes two line-defect waveguides for input and output, and a uniquely engineered Eye-shaped cavity that holds the analyte as embedded rods. These rods are strategically arranged along the Eye-shaped boundary and the central area resembling an iris, facilitating accurate detection through resonance wavelength shifts triggered by changes in the refractive index. The biosensor demonstrates excellent transmission efficiency (69.7%–99.9%), high sensitivity (236–243 nm/RIU), and a strong quality factor (15,764–87,070), ensuring sharp and clearly defined resonance peaks. A key advantage of the design is its linear response to refractive index variations, which enhances detection accuracy and supports reliable real-time biosensing. Moreover, the sensor maintains stable performance across a wide temperature range (25 °C to 75 °C) and exhibits robust tolerance to fabrication variations. These features validate the biosensor’s precision for biomedical diagnostics.

This work describes a microstrip log-periodic dipole array (MLPDA) antenna that uses metamaterials and operates across the whole Ka-band. The suggested MLPDA antenna layout provides a wide bandwidth with fewer dipole elements than traditional MLPDA antennas while maintaining the same resonance frequencies. To reduce size while covering a wide operational spectrum, the antenna design includes bending dipoles as radiating elements, as well as an incomplete ground plane. Furthermore, the proposed MLPDA antenna’s energy loss has been reduced while boosting its signal strength (gain) by inserting a metamaterial-based structure in front of it at a certain distance and on the same substrate. The antenna was initially designed using fundamental mathematical equations, then simulated using the finite difference time domain method, and subsequently fabricated and characterized. Additionally, particle swarm optimization (PSO) was used to optimize the design requirements of the metamaterial layer, partial ground, and bending elements. Simulation and experimental results show good agreement, demonstrating that the optimized MLPDAfunctions over a wide bandwidth of 25.9 GHz to 45.2 GHz, with slight variances due to fabrication tolerances and measurement conditions. Moreover, the reduction in energy loss achieved by metamaterials directly leads to the stated 4 dB gain increase across the whole Ka-band, improving efficiency and communication range in a variety of applications, including satellite communications, 5G systems, and radar.

A wearable textile bra-tenna system based on dual-polarization sensors for breast cancer (BC) detection is presented in this paper. The core concept behind our work is to investigate which type of polarization is most effective for BC detection, using the combination of orthogonal polarization signals with machine learning (ML) techniques to enhance detection accuracy. The bra-tenna sensors have a bandwidth ranging from 2–12 GHz. To complement the proposed system, detection based on machine learning algorithms (MLAs) is developed and tested to enhance its functionality. Using scattered signals at different polarizations, the bra-tenna system uses MLAs to predict BC in its early stages. Classification techniques are highly effective for data classification, especially in the biomedical field. Two scenarios are considered: Scenario 1, where the system detects a tumor or non-tumor, and Scenario 2, where the system detects three classes of one, two, and non-tumors. This confirms that MLAs can detect tumors as small as 10 mm. ML techniques, including eight algorithms such as the Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Methods (GBMs), Decision Tree (DT) classifier, Ada Boost (AD), CatBoost, Extreme Gradient Boosting (XG Boost), and Logistic Regression (LR), are applied to this balanced dataset. For optimal analysis of the BC, a performance evaluation is performed. Notably, SVM achieves outstanding performance in both scenarios, with metrics such as its F1 score, recall, accuracy, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), and precision all exceeding 90%, helping doctors to effectively investigate BC. Furthermore, the Horizontal-Horizontal (HH) sensor configuration achieved the highest accuracy of 98% and 99% for SVMs in the two scenarios, respectively.

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.

This work proposes a slotted microstrip patch antenna with an inset feed and defective ground structure (DGS). The proposed antenna is built with Roger-RT/5880 (Ԑr=2.2) as the substrate material for X-band application with a resonant frequency of 10 GHz. The proposed design has been simulated using Finite Element Method (FEM) and the results of bandwidth and gain read about 700MHz and 8dB; respectively. The suggested design is compared with previously published equivalent designs in light of the most recent research. The comparison reveal that that the suggested design with tuned dimensions offers higher gain and wider bandwidth than what has already been described. Additionally, the proposed antenna is employed for detecting the breast tissue tumors with different radii through calculating the return loss curves of the antenna. The antennas’ position in relation to the utilized breast phantom is optimized to effectively identify a tumor with a minimum radius of 3 mm as well as considering the special safety issues to be less than1.6 W/Kg for a 1g of cube-shaped tissue.

A new design for a tunable multi-channel plasmonic bandpass filter was numerically investigated using the two-dimensional finite element method (2D-FEM). The proposed multi-channel plasmonic bandpass filter consists of a metal-insulator-metal waveguide (MIM-WG) and double-sided arrow-shaped cavities. Silver (Ag) and a non-linear optical medium (InGaAsP) are used in the designed filter. InGaAsP fills the bus waveguide and arrow-shaped cavities. The refractive index of InGaAsP is sensitive to the incident light intensity, therefore the resonance wavelengths can be controlled. Utilizing different incident light intensities (such as 10¹⁷ v²/m² and 2 × 10¹⁷ v²/m²) on the InGaAsP, the filter wavelengths can be tuned over a range from 600 nm to 1200 nm. The proposed filter with a confinement area of 0.5 μm² can be used in wavelength division multiplexing (WDM), photonic systems, coloring filters, sensing, and 5G+ communication.

Recently, there has been considerable growth in the internet of things (IoT)-based healthcare applications; however, they suffer from a lack of intrusion detection systems (IDS). Leveraging recent technologies, such as machine learning (ML), edge computing, and blockchain, can provide suitable and strong security solutions for preserving the privacy of medical data. In this paper, FIDChain IDS is proposed using lightweight artificial neural networks (ANN) in a federated learning (FL) way to ensure healthcare data privacy preservation with the advances of blockchain technology that provides a distributed ledger for aggregating the local weights and then broadcasting the updated global weights after averaging, which prevents poisoning attacks and provides full transparency and immutability over the distributed system with negligible overhead. Applying the detection model at the edge protects the cloud if an attack happens, as it blocks the data from its gateway with smaller detection time and lesser computing and processing capacity as FL deals with smaller sets of data. The ANN and eXtreme Gradient Boosting (XGBoost) models were evaluated using the BoT-IoT dataset. The results show that ANN models have higher accuracy and better performance with the heterogeneity of data in IoT devices, such as intensive care unit (ICU) in healthcare systems. Testing the FIDChain with different datasets (CSE-CIC-IDS2018, Bot Net IoT, and KDD Cup 99) reveals that the BoT-IoT dataset has the most stable and accurate results for testing IoT applications, such as those used in healthcare systems.